In the past ten years, there has been a substantial increase in the use of clinical PET for oncological imaging applications, which has primarily been driven by the increased availability of 18F-FDG. In order to maximize sensitivity, the recent trend in PET scanner design is for faster and brighter scintillators, larger axial dimensions, and acquisition of fully three-dimensional (3D) data, without the use of septa. These trends result in an increased complexity of the reconstruction process. The overall goal of this proposal is to develop 3D image reconstruction methods for PET that can provide improved diagnostic accuracy. The most common approach for clinical 3D PET reconstruction is to use a re-binning method combined with 2D reconstruction. While this approach can be implemented with low computational cost, it models the data as line integrals through the object, and thus cannot accurately account for the spatially variant detector response in PET. In actuality, there are a number of physical effects in the imaging process that invalidate this line integral assumption. These include such effects as: positron range, non-collinearity, spatially variant geometric efficiency, inter-crystal penetration, crystal scatter, and uncertainties in accurately locating the position of interaction within the detector block. In this project, we propose to develop and investigate an approach for 3D PET that use alternative basis functions (as opposed to voxel basis functions) to describe the object of interest. These basis functions takes full advantage of the symmetries present in the PET geometry resulting in a system response matrix with block circulant properties. These properties make it possible to implement the reconstruction algorithm with storage of the entire 3D system response matrix in memory, and with very fast computation time. An accurate Monte Carlo simulation code (GATE) will be used to compute the 3D system response matrix. Psychophysical observer studies, using clinical images, will be conducted to evaluate improvements in sub 1 cm tumor detection with different reconstruction methods. If the proposed reconstruction methods are successful in their intent, the care of patients with suspected or known cancer will be improved. PUBLIC HEALTH RELEVANCE: The overall goal of this proposal is to develop 3D image reconstruction methods for PET that can provide improved diagnostic accuracy. Psychophysical observer studies, using clinical images, will be conducted to evaluate improvements in sub 1 cm tumor detection with different reconstruction methods. If the proposed reconstruction methods are successful in their intent, the care of patients with suspected or known cancer will be improved.